Denis Boyda
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View article: Progress in Normalizing Flows for 4d Gauge Theories
Progress in Normalizing Flows for 4d Gauge Theories Open
Normalizing flows have arisen as a tool to accelerate Monte Carlo sampling for lattice field theories. This work reviews recent progress in applying normalizing flows to 4-dimensional nonabelian gauge theories, focusing on two advancements…
View article: Progress in Normalizing Flows for 4d Gauge Theories
Progress in Normalizing Flows for 4d Gauge Theories Open
Normalizing flows have arisen as a tool to accelerate Monte Carlo sampling for lattice field theories. This work reviews recent progress in applying normalizing flows to 4-dimensional nonabelian gauge theories, focusing on two advancements…
View article: Applications of flow models to the generation of correlated lattice QCD ensembles
Applications of flow models to the generation of correlated lattice QCD ensembles Open
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these correlatio…
View article: Multiscale Normalizing Flows for Gauge Theories
Multiscale Normalizing Flows for Gauge Theories Open
Scale separation is an important physical principle that has previously enabled algorithmic advances such as multigrid solvers. Previous work on normalizing flows has been able to utilize scale separation in the context of scalar field the…
View article: Practical applications of machine-learned flows on gauge fields
Practical applications of machine-learned flows on gauge fields Open
Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an ope…
View article: Practical applications of machine-learned flows on gauge fields
Practical applications of machine-learned flows on gauge fields Open
Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an ope…
View article: Multiscale Normalizing Flows for Gauge Theories
Multiscale Normalizing Flows for Gauge Theories Open
Scale separation is an important physical principle that has previously enabled algorithmic advances such as multigrid solvers. Previous work on normalizing flows has been able to utilize scale separation in the context of scalar field the…
View article: Applications of flow models to the generation of correlated lattice QCD ensembles
Applications of flow models to the generation of correlated lattice QCD ensembles Open
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these correlatio…
View article: Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Normalizing flows for lattice gauge theory in arbitrary space-time dimension Open
Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions. We report new algorithmic developments of gauge-equivariant flow a…
View article: Sampling QCD field configurations with gauge-equivariant flow models
Sampling QCD field configurations with gauge-equivariant flow models Open
Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gauge field configurations in simple lattice field theories. A …
View article: Aspects of scaling and scalability for flow-based sampling of lattice QCD
Aspects of scaling and scalability for flow-based sampling of lattice QCD Open
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the s…
View article: Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions Open
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant. This is the default approach in state-of-the-art la…
View article: Sampling QCD field configurations with gauge-equivariant flow models
Sampling QCD field configurations with gauge-equivariant flow models Open
Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gauge field configurations in simple lattice field theories. A …
View article: Flow-based sampling in the lattice Schwinger model at criticality
Flow-based sampling in the lattice Schwinger model at criticality Open
Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodynamics and the Schwinger model. In this work, we provide a nu…
View article: Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions Open
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant. This is the default approach in state-of-the-art la…
View article: Applying machine learning methods to prediction problems of lattice observables
Applying machine learning methods to prediction problems of lattice observables Open
We discuss the prediction of critical behavior of lattice observables in SU(2) and SU(3) gauge theories. We show that feed-forward neural network, trained on the lattice configurations of gauge fields as input data, finds correlations with…
View article: Report on 2112.07865v2
Report on 2112.07865v2 Open
We discuss the prediction of critical behavior of lattice observables in SU(2) and SU(3) gauge theories.We show that feed-forward neural network, trained on the lattice configurations of gauge fields as input data, finds correlations with …
View article: Flow-based sampling in the lattice Schwinger model at criticality
Flow-based sampling in the lattice Schwinger model at criticality Open
Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodynamics and the Schwinger model. In this work, we provide a nu…
View article: Applications of Machine Learning to Lattice Quantum Field Theory
Applications of Machine Learning to Lattice Quantum Field Theory Open
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In this white paper for the Snowmass community planning process,…
View article: Flow-based sampling for fermionic lattice field theories
Flow-based sampling for fermionic lattice field theories Open
Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact. In the context of lattice field theory, proof-of-…
View article: Flow-based sampling for multimodal and extended-mode distributions in lattice field theory
Flow-based sampling for multimodal and extended-mode distributions in lattice field theory Open
Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of training- and architecture-b…
View article: Introduction to Normalizing Flows for Lattice Field Theory
Introduction to Normalizing Flows for Lattice Field Theory Open
This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The ideas and approaches proposed in arXiv:1904.12072, arXiv:2…
View article: Finding the deconfinement temperature in lattice Yang-Mills theories from outside the scaling window with machine learning
Finding the deconfinement temperature in lattice Yang-Mills theories from outside the scaling window with machine learning Open
We study the machine learning techniques applied to the lattice gauge theory's critical behavior, particularly to the confinement/deconfinement phase transition in the SU(2) and SU(3) gauge theories. We find that the neural network, traine…
View article: Equivariant Flow-Based Sampling for Lattice Gauge Theory
Equivariant Flow-Based Sampling for Lattice Gauge Theory Open
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and…
View article: Equivariant flow-based sampling for lattice gauge theory
Equivariant flow-based sampling for lattice gauge theory Open
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and…
View article: Canonical partition functions in lattice QCD at finite density and temperature
Canonical partition functions in lattice QCD at finite density and temperature Open
We study the QCD matter at finite density and temperature. The sign problem is overcome by our new cannonical approach. We compare RHIC energy scan data and lattice QCD simulations.
View article: New way of collision experiment data analysis based on Grand Canonical Distribution and Lattice QCD data
New way of collision experiment data analysis based on Grand Canonical Distribution and Lattice QCD data Open
We propose new way of heavy ion collisions experiment data analysis. We analyze physical parameters of fireball created in RHIC experiment based on Grand Canonical Distribution and different Lattice QCD data available at the moment. Our re…